May 03, 2006
Research Associate Professor John Preisser was the lead biostatistician on an award-winning research project on migrant farm workers. The study “Reducing the Impact of Green Tobacco Sickness among Latino Farm workers” was the recipient of the 2006 National Occupational Research Agenda (NORA) Innovative Research Award ( The NORA Liaison Committee, in cooperation with the National Institute for Occupational Safety and Health (NIOSH/CDC), presented the award at the fifth biannual NORA Symposium in Washington, DC on April 18-20, 2006 to Thomas Arcury and Sara Quandt, principal investigator (PI) and co-PI of the project, respectively, and Professors in the Wake Forest University School of Medicine. This was the first time NIOSH gave the award, and Preisser and colleagues were surprised that it went to a project dealing with immigrant workers.Green Tobacco Sickness (GTS) often affects farm workers who handle wet tobacco leaves and may lead to symptoms such as dizziness, nausea, and vomiting that can lead to life-threatening dehydration. Latino tobacco workers are especially at risk since they supply the majority of the tobacco labor in the US. The epidemiological study conducted in eastern North Carolina was the first to document the prevalence of symptoms in a cohort of farm workers, and to examine cases in depth to determine risk factors for illness. The result was the first body of scholarly work on GTS epidemiology and the production of culturally appropriate education materials for both farm workers and medical personnel who treat GTS. Some of the statistical challenges addressed by Dr. Preisser and colleagues included establishing a case definition of GTS, consideration of high attrition rates, and handling of clustered data. The research resulted in eleven peer-reviewed scientific articles on GTS including eight co-authored by Dr. Preisser, among these, a 2003 paper in the American Journal of Epidemiology for which he was the principle author, “Detecting Patterns of Occupational Illness Clustering with Alternating Logistic Regressions Applied to Longitudinal Data.”

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